There are a variety of situations in which there is a desire to create a map defining a complete layout of locations within a geographic location. One such application is determining an indoor layout within a building. As one example, it may be desirable to create a complete layout of stores within a shopping mall. As another example, it may be desirable to create a layout of rooms within a hospital. As another example, it may be desirable to create a complete layout of a college campus. As yet another example, it may be desirable to create a layout of aisles within an individual store.
However, creating maps of indoor places is a difficult problem. Traditionally this problem has been solved in a very brute force way, with lots of manual effort in creating a map of each new place. Using crowd sourced data is a possible solution to this problem. In a crowd sourcing approach, data is obtained from many different users and then combined to generate a map. For example, individual users of mobile devices may provide data from their mobile devices as they move around a geographic location, which is then aggregated. However, crowd sourcing suffers from potential coverage problems. The crowd sourced data will tend to reflect the popularity of different areas in terms of foot traffic. As a consequence, less popular places may require an excessively long period of time before crowd sourced data becomes available.